Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Substantial health disparities exist between Māori--the indigenous people of Aotearoa New Zealand--and non-Māori New Zealanders. This article explores the experience and impact of racism on Māori registered nurses within the New Zealand health system. METHOD: The narratives of 15 Māori registered nurses were analyzed to identify the effects of racism. This Māori nursing cohort and the data on racism form a secondary analysis drawn from a larger research project investigating the experiences of indigenous health workers in New Zealand and Canada. Jones's levels of racism were utilized as a coding frame for the structural analysis of the transcribed Māori registered nurse interviews. RESULTS: Participants experienced racism on institutional, interpersonal, and internalized levels, leading to marginalization and being overworked yet undervalued. DISCUSSION AND CONCLUSIONS: Māori registered nurses identified a lack of acknowledgement of dual nursing competencies: while their clinical skills were validated, their cultural skills-their skills in Hauora Māori--were often not. Experiences of racism were a commonality. Racism--at every level--can be seen as highly influential in the recruitment, training, retention, and practice of Māori registered nurses. IMPLICATIONS FOR PRACTICE: The nursing profession in New Zealand and other countries of indigenous peoples needs to acknowledge the presence of racism within training and clinical environments as well as supporting indigenous registered nurses to develop and implement indigenous dual cultural-clinical competencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it